Overview

Dataset statistics

Number of variables49
Number of observations115609
Missing cells176925
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.1 MiB
Average record size in memory400.0 B

Variable types

Text12
Categorical9
DateTime8
Numeric20

Alerts

customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
order_purchase_month is highly overall correlated with order_purchase_year_monthHigh correlation
order_purchase_year is highly overall correlated with order_purchase_year_monthHigh correlation
order_purchase_year_month is highly overall correlated with order_purchase_month and 1 other fieldsHigh correlation
payment_value is highly overall correlated with priceHigh correlation
price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_volume_cm and 1 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_volume_cm and 2 other fieldsHigh correlation
product_volume_cm is highly overall correlated with product_height_cm and 3 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 4 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 2 other fieldsHigh correlation
seller_state is highly overall correlated with seller_zip_code_prefixHigh correlation
seller_zip_code_prefix is highly overall correlated with seller_stateHigh correlation
order_status is highly imbalanced (93.4%)Imbalance
seller_state is highly imbalanced (63.2%)Imbalance
order_delivered_carrier_date has 1195 (1.0%) missing valuesMissing
order_delivered_customer_date has 2400 (2.1%) missing valuesMissing
review_comment_title has 101808 (88.1%) missing valuesMissing
review_comment_message has 66703 (57.7%) missing valuesMissing
time_to_delivery has 2400 (2.1%) missing valuesMissing
delivery_against_estimated has 2400 (2.1%) missing valuesMissing
review_response_time is highly skewed (γ1 = 24.09891986)Skewed
review_response_time has 28066 (24.3%) zerosZeros
delivery_against_estimated has 1468 (1.3%) zerosZeros
order_purchase_hour has 2835 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-06 04:42:34.182917
Analysis finished2023-12-06 04:43:41.471557
Duration1 minute and 7.29 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:41.696420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
5a3b1c29a49756e75f1ef513383c0c12 22
 
< 0.1%
Other values (96506) 115311
99.7%
2023-12-06T11:43:42.061790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311582
62.5%
Lowercase Letter 1387906
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 232347
10.1%
6 232183
10.0%
3 231489
10.0%
1 231448
10.0%
7 231435
10.0%
8 231366
10.0%
2 230768
10.0%
9 230559
10.0%
0 230074
10.0%
5 229913
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 232260
16.7%
e 231974
16.7%
c 231513
16.7%
a 231122
16.7%
f 230865
16.6%
d 230172
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2311582
62.5%
Latin 1387906
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 232347
10.1%
6 232183
10.0%
3 231489
10.0%
1 231448
10.0%
7 231435
10.0%
8 231366
10.0%
2 230768
10.0%
9 230559
10.0%
0 230074
10.0%
5 229913
9.9%
Latin
ValueCountFrequency (%)
b 232260
16.7%
e 231974
16.7%
c 231513
16.7%
a 231122
16.7%
f 230865
16.6%
d 230172
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%
Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:42.380856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d329 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
be1c4e52bb71e0c54b11a26b8e8d59f2 22
 
< 0.1%
Other values (96506) 115311
99.7%
2023-12-06T11:43:42.805233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311390
62.5%
Lowercase Letter 1388098
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 231848
10.0%
5 231684
10.0%
6 231574
10.0%
1 231546
10.0%
8 231191
10.0%
9 231116
10.0%
3 231114
10.0%
7 231035
10.0%
4 230174
10.0%
0 230108
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 231815
16.7%
c 231607
16.7%
e 231206
16.7%
a 231174
16.7%
d 231155
16.7%
b 231141
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2311390
62.5%
Latin 1388098
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 231848
10.0%
5 231684
10.0%
6 231574
10.0%
1 231546
10.0%
8 231191
10.0%
9 231116
10.0%
3 231114
10.0%
7 231035
10.0%
4 230174
10.0%
0 230108
10.0%
Latin
ValueCountFrequency (%)
f 231815
16.7%
c 231607
16.7%
e 231206
16.7%
a 231174
16.7%
d 231155
16.7%
b 231141
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

order_status
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
delivered
113210 
shipped
 
1138
canceled
 
536
invoiced
 
358
processing
 
357
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.9757631
Min length7

Characters and Unicode

Total characters1037679
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 113210
97.9%
shipped 1138
 
1.0%
canceled 536
 
0.5%
invoiced 358
 
0.3%
processing 357
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Length

2023-12-06T11:43:42.904220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T11:43:42.989470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
delivered 113210
97.9%
shipped 1138
 
1.0%
canceled 536
 
0.5%
invoiced 358
 
0.3%
processing 357
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1037679
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1037679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1037679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%
Distinct95989
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-09-04 21:15:19
Maximum2018-09-03 09:06:57
2023-12-06T11:43:43.066904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:43.150256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct88332
Distinct (%)76.4%
Missing14
Missing (%)< 0.1%
Memory size1.8 MiB
Minimum2016-10-04 09:43:32
Maximum2018-09-03 17:40:06
2023-12-06T11:43:43.242813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:43.325300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct79241
Distinct (%)69.3%
Missing1195
Missing (%)1.0%
Memory size1.8 MiB
Minimum2016-10-08 10:34:01
Maximum2018-09-11 19:48:28
2023-12-06T11:43:43.410749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:43.497218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct93702
Distinct (%)82.8%
Missing2400
Missing (%)2.1%
Memory size1.8 MiB
Minimum2016-10-11 13:46:32
Maximum2018-10-17 13:22:46
2023-12-06T11:43:43.589584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:43.676983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct449
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-10-20 00:00:00
Maximum2018-10-25 00:00:00
2023-12-06T11:43:43.773044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:43.876433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct93396
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:44.170789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79377 ?
Unique (%)68.7%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e7 24
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
Other values (93386) 115286
99.7%
2023-12-06T11:43:44.539510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2312012
62.5%
Lowercase Letter 1387476
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 232253
10.0%
1 231813
10.0%
3 231341
10.0%
8 231179
10.0%
2 231112
10.0%
5 231087
10.0%
9 231084
10.0%
7 231012
10.0%
0 230821
10.0%
4 230310
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 231885
16.7%
a 231488
16.7%
d 231353
16.7%
e 231271
16.7%
f 230923
16.6%
c 230556
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2312012
62.5%
Latin 1387476
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 232253
10.0%
1 231813
10.0%
3 231341
10.0%
8 231179
10.0%
2 231112
10.0%
5 231087
10.0%
9 231084
10.0%
7 231012
10.0%
0 230821
10.0%
4 230310
10.0%
Latin
ValueCountFrequency (%)
b 231885
16.7%
a 231488
16.7%
d 231353
16.7%
e 231271
16.7%
f 230923
16.6%
c 230556
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

customer_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct14907
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35061.538
Minimum1003
Maximum99980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:44.644556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3286
Q111310
median24241
Q358745
95-th percentile90620
Maximum99980
Range98977
Interquartile range (IQR)47435

Descriptive statistics

Standard deviation29841.672
Coefficient of variation (CV)0.85112273
Kurtosis-0.78410177
Mean35061.538
Median Absolute Deviation (MAD)16231
Skewness0.78436422
Sum4.0534293 × 109
Variance8.9052537 × 108
MonotonicityNot monotonic
2023-12-06T11:43:44.734251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 154
 
0.1%
22793 151
 
0.1%
22790 150
 
0.1%
24230 138
 
0.1%
22775 126
 
0.1%
35162 124
 
0.1%
29101 112
 
0.1%
11740 110
 
0.1%
13087 107
 
0.1%
36570 104
 
0.1%
Other values (14897) 114333
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 3
 
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 2
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 1
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
99920 1
 
< 0.1%
Distinct4093
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:45.071410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.332855
Min length3

Characters and Unicode

Total characters1194571
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)0.9%

Sample

1st rowSao Paulo
2nd rowSao Paulo
3rd rowSao Paulo
4th rowSao Paulo
5th rowSao Paulo
ValueCountFrequency (%)
sao 24620
 
12.1%
paulo 18348
 
9.1%
de 11267
 
5.6%
rio 9627
 
4.8%
janeiro 8022
 
4.0%
do 4964
 
2.4%
belo 3269
 
1.6%
horizonte 3224
 
1.6%
brasilia 2444
 
1.2%
porto 1942
 
1.0%
Other values (3268) 114916
56.7%
2023-12-06T11:43:45.458958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 188530
15.8%
o 145141
12.2%
87034
 
7.3%
i 85625
 
7.2%
e 76426
 
6.4%
r 75382
 
6.3%
u 51224
 
4.3%
n 49130
 
4.1%
l 48521
 
4.1%
s 37481
 
3.1%
Other values (46) 350077
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 903815
75.7%
Uppercase Letter 203181
 
17.0%
Space Separator 87034
 
7.3%
Dash Punctuation 281
 
< 0.1%
Other Punctuation 258
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 188530
20.9%
o 145141
16.1%
i 85625
9.5%
e 76426
8.5%
r 75382
 
8.3%
u 51224
 
5.7%
n 49130
 
5.4%
l 48521
 
5.4%
s 37481
 
4.1%
t 35486
 
3.9%
Other values (16) 110869
12.3%
Uppercase Letter
ValueCountFrequency (%)
S 35724
17.6%
P 30362
14.9%
D 22298
11.0%
C 17611
8.7%
J 14274
 
7.0%
R 13148
 
6.5%
B 12820
 
6.3%
A 8508
 
4.2%
M 7204
 
3.5%
G 7169
 
3.5%
Other values (15) 34063
16.8%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
87034
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%
Other Punctuation
ValueCountFrequency (%)
' 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1106996
92.7%
Common 87575
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 188530
17.0%
o 145141
13.1%
i 85625
 
7.7%
e 76426
 
6.9%
r 75382
 
6.8%
u 51224
 
4.6%
n 49130
 
4.4%
l 48521
 
4.4%
s 37481
 
3.4%
S 35724
 
3.2%
Other values (41) 313812
28.3%
Common
ValueCountFrequency (%)
87034
99.4%
- 281
 
0.3%
' 258
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 188530
15.8%
o 145141
12.2%
87034
 
7.3%
i 85625
 
7.2%
e 76426
 
6.4%
r 75382
 
6.3%
u 51224
 
4.3%
n 49130
 
4.1%
l 48521
 
4.1%
s 37481
 
3.1%
Other values (46) 350077
29.3%

customer_state
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
48797 
RJ
14987 
MG
13429 
RS
6413 
PR
5879 
Other values (22)
26104 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231218
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 48797
42.2%
RJ 14987
 
13.0%
MG 13429
 
11.6%
RS 6413
 
5.5%
PR 5879
 
5.1%
SC 4218
 
3.6%
BA 3942
 
3.4%
DF 2449
 
2.1%
GO 2359
 
2.0%
ES 2300
 
2.0%
Other values (17) 10836
 
9.4%

Length

2023-12-06T11:43:45.542202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 48797
42.2%
rj 14987
 
13.0%
mg 13429
 
11.6%
rs 6413
 
5.5%
pr 5879
 
5.1%
sc 4218
 
3.6%
ba 3942
 
3.4%
df 2449
 
2.1%
go 2359
 
2.0%
es 2300
 
2.0%
Other values (17) 10836
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231218
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 231218
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194535
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:45.608344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68592588
Coefficient of variation (CV)0.57421998
Kurtosis93.440808
Mean1.194535
Median Absolute Deviation (MAD)0
Skewness7.195206
Sum138099
Variance0.47049432
MonotonicityNot monotonic
2023-12-06T11:43:45.680778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
6 256
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 57
 
< 0.1%
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
6 256
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 4
 
< 0.1%
14 6
< 0.1%
13 7
< 0.1%
12 12
< 0.1%
Distinct32171
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:45.930305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16932 ?
Unique (%)14.6%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 533
 
0.5%
99a4788cb24856965c36a24e339b6058 517
 
0.4%
422879e10f46682990de24d770e7f83d 507
 
0.4%
389d119b48cf3043d311335e499d9c6b 405
 
0.4%
368c6c730842d78016ad823897a372db 395
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 354
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 324
 
0.3%
154e7e31ebfa092203795c972e5804a6 294
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7 276
 
0.2%
Other values (32161) 111615
96.5%
2023-12-06T11:43:46.265708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2321899
62.8%
Lowercase Letter 1377589
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 237911
10.2%
9 235951
10.2%
7 233105
10.0%
8 232719
10.0%
4 231409
10.0%
2 230954
9.9%
6 230872
9.9%
0 230822
9.9%
5 229692
9.9%
1 228464
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 233656
17.0%
a 231308
16.8%
c 231075
16.8%
b 229587
16.7%
d 227350
16.5%
f 224613
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2321899
62.8%
Latin 1377589
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 237911
10.2%
9 235951
10.2%
7 233105
10.0%
8 232719
10.0%
4 231409
10.0%
2 230954
9.9%
6 230872
9.9%
0 230822
9.9%
5 229692
9.9%
1 228464
9.8%
Latin
ValueCountFrequency (%)
e 233656
17.0%
a 231308
16.8%
c 231075
16.8%
b 229587
16.7%
d 227350
16.5%
f 224613
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%
Distinct3028
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:46.512007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2128
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2111
 
1.8%
1f50f920176fa81dab994f9023523100 2009
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1885
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1656
 
1.4%
955fee9216a65b617aa5c0531780ce60 1517
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1465
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1454
 
1.3%
7a67c85e85bb2ce8582c35f2203ad736 1236
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1233
 
1.1%
Other values (3018) 98915
85.6%
2023-12-06T11:43:46.844508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2339688
63.2%
Lowercase Letter 1359800
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 251950
10.8%
4 243091
10.4%
6 238193
10.2%
0 237394
10.1%
3 235558
10.1%
9 228810
9.8%
2 227464
9.7%
8 226366
9.7%
5 225942
9.7%
7 224920
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 243876
17.9%
a 236721
17.4%
b 235816
17.3%
e 216428
15.9%
f 214706
15.8%
d 212253
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2339688
63.2%
Latin 1359800
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 251950
10.8%
4 243091
10.4%
6 238193
10.2%
0 237394
10.1%
3 235558
10.1%
9 228810
9.8%
2 227464
9.7%
8 226366
9.7%
5 225942
9.7%
7 224920
9.6%
Latin
ValueCountFrequency (%)
c 243876
17.9%
a 236721
17.4%
b 235816
17.3%
e 216428
15.9%
f 214706
15.8%
d 212253
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%
Distinct91386
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-09-19 00:15:34
Maximum2020-04-09 22:35:08
2023-12-06T11:43:46.940575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:47.054299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct5879
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.61985
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:47.175682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation182.65348
Coefficient of variation (CV)1.5142904
Kurtosis107.9051
Mean120.61985
Median Absolute Deviation (MAD)42
Skewness7.6154182
Sum13944740
Variance33362.292
MonotonicityNot monotonic
2023-12-06T11:43:47.293292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2574
 
2.2%
69.9 2096
 
1.8%
49.9 2013
 
1.7%
89.9 1604
 
1.4%
99.9 1505
 
1.3%
29.9 1362
 
1.2%
39.9 1325
 
1.1%
79.9 1266
 
1.1%
19.9 1263
 
1.1%
29.99 1204
 
1.0%
Other values (5869) 99397
86.0%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
3.54 1
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%
3999 2
< 0.1%

freight_value
Real number (ℝ)

Distinct6954
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.05688
Minimum0
Maximum409.68
Zeros387
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:47.422669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.32
Q321.21
95-th percentile45.31
Maximum409.68
Range409.68
Interquartile range (IQR)8.13

Descriptive statistics

Standard deviation15.836184
Coefficient of variation (CV)0.78956371
Kurtosis58.250048
Mean20.05688
Median Absolute Deviation (MAD)3.63
Skewness5.5602128
Sum2318755.8
Variance250.78473
MonotonicityNot monotonic
2023-12-06T11:43:47.541156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3754
 
3.2%
7.78 2281
 
2.0%
11.85 1948
 
1.7%
14.1 1939
 
1.7%
18.23 1599
 
1.4%
7.39 1554
 
1.3%
16.11 1185
 
1.0%
15.23 1041
 
0.9%
8.72 950
 
0.8%
16.79 902
 
0.8%
Other values (6944) 98456
85.2%
ValueCountFrequency (%)
0 387
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 3
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.093747
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:47.650942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72984871
Coefficient of variation (CV)0.66729206
Kurtosis350.44021
Mean1.093747
Median Absolute Deviation (MAD)0
Skewness16.001768
Sum126447
Variance0.53267914
MonotonicityNot monotonic
2023-12-06T11:43:47.751141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
6 127
 
0.1%
7 88
 
0.1%
8 58
 
0.1%
9 47
 
< 0.1%
10 40
 
< 0.1%
Other values (19) 163
 
0.1%
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
6 127
 
0.1%
7 88
 
0.1%
8 58
 
0.1%
9 47
 
< 0.1%
10 40
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Credit Card
85278 
Boleto
22510 
Voucher
 
6162
Debit Card
 
1659

Length

Max length11
Median length11
Mean length9.7989084
Min length6

Characters and Unicode

Total characters1132842
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowVoucher
3rd rowVoucher
4th rowCredit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Credit Card 85278
73.8%
Boleto 22510
 
19.5%
Voucher 6162
 
5.3%
Debit Card 1659
 
1.4%

Length

2023-12-06T11:43:47.850983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T11:43:47.950129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
card 86937
42.9%
credit 85278
42.1%
boleto 22510
 
11.1%
voucher 6162
 
3.0%
debit 1659
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r 178377
15.7%
C 172215
15.2%
d 172215
15.2%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
l 22510
 
2.0%
Other values (7) 50476
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 843359
74.4%
Uppercase Letter 202546
 
17.9%
Space Separator 86937
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 178377
21.2%
d 172215
20.4%
e 115609
13.7%
t 109447
13.0%
i 86937
10.3%
a 86937
10.3%
o 51182
 
6.1%
l 22510
 
2.7%
u 6162
 
0.7%
c 6162
 
0.7%
Other values (2) 7821
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 172215
85.0%
B 22510
 
11.1%
V 6162
 
3.0%
D 1659
 
0.8%
Space Separator
ValueCountFrequency (%)
86937
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1045905
92.3%
Common 86937
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 178377
17.1%
C 172215
16.5%
d 172215
16.5%
e 115609
11.1%
t 109447
10.5%
i 86937
8.3%
a 86937
8.3%
o 51182
 
4.9%
l 22510
 
2.2%
B 22510
 
2.2%
Other values (6) 27966
 
2.7%
Common
ValueCountFrequency (%)
86937
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1132842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 178377
15.7%
C 172215
15.2%
d 172215
15.2%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
l 22510
 
2.0%
Other values (7) 50476
 
4.5%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9462326
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:48.043322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7810871
Coefficient of variation (CV)0.94394691
Kurtosis2.5137681
Mean2.9462326
Median Absolute Deviation (MAD)1
Skewness1.6181717
Sum340611
Variance7.7344456
MonotonicityNot monotonic
2023-12-06T11:43:48.141477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
10 6785
 
5.9%
5 5928
 
5.1%
8 5013
 
4.3%
6 4546
 
3.9%
7 1789
 
1.5%
9 710
 
0.6%
Other values (14) 429
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
5 5928
 
5.1%
6 4546
 
3.9%
7 1789
 
1.5%
8 5013
 
4.3%
9 710
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 20
 
< 0.1%
18 38
< 0.1%
17 7
 
< 0.1%
16 7
 
< 0.1%
15 91
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

HIGH CORRELATION 

Distinct28657
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.38738
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:48.254499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.23
Q160.87
median108.05
Q3189.48
95-th percentile514.98
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.61

Descriptive statistics

Standard deviation265.87397
Coefficient of variation (CV)1.5423053
Kurtosis524.94523
Mean172.38738
Median Absolute Deviation (MAD)56.67
Skewness14.306544
Sum19929532
Variance70688.967
MonotonicityNot monotonic
2023-12-06T11:43:48.370034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 338
 
0.3%
100 285
 
0.2%
20 283
 
0.2%
77.57 249
 
0.2%
35 163
 
0.1%
73.34 157
 
0.1%
30 134
 
0.1%
116.94 130
 
0.1%
56.78 120
 
0.1%
155.14 119
 
0.1%
Other values (28647) 113631
98.3%
ValueCountFrequency (%)
0 6
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
< 0.1%
0.11 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 4
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%
4513.32 1
 
< 0.1%
Distinct96319
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:48.666832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83596 ?
Unique (%)72.3%

Sample

1st rowa54f0611adc9ed256b57ede6b6eb5114
2nd rowa54f0611adc9ed256b57ede6b6eb5114
3rd rowa54f0611adc9ed256b57ede6b6eb5114
4th rowb46f1e34512b0f4c74a72398b03ca788
5th rowdc90f19c2806f1abba9e72ad3c350073
ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e 63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f 38
 
< 0.1%
f28281373ab8815bafafe371218f02ce 29
 
< 0.1%
8823bba1e3301fee652eb06de8ef9435 26
 
< 0.1%
b79b22bb50f78f1afe361661011fd892 24
 
< 0.1%
b5292206f96cd5d97359940203a0b510 24
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf634 24
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e 24
 
< 0.1%
7e568736c98c553aea896a5dca746d5a 22
 
< 0.1%
6fe49ee0a2b00dddf7ebddb5847f9283 21
 
< 0.1%
Other values (96309) 115314
99.7%
2023-12-06T11:43:49.086050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 232033
 
6.3%
6 232011
 
6.3%
8 231576
 
6.3%
5 231572
 
6.3%
d 231486
 
6.3%
f 231421
 
6.3%
b 231386
 
6.3%
1 231303
 
6.3%
0 231222
 
6.3%
2 231089
 
6.2%
Other values (6) 1384389
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311211
62.5%
Lowercase Letter 1388277
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 232011
10.0%
8 231576
10.0%
5 231572
10.0%
1 231303
10.0%
0 231222
10.0%
2 231089
10.0%
7 230957
10.0%
9 230772
10.0%
3 230468
10.0%
4 230241
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 232033
16.7%
d 231486
16.7%
f 231421
16.7%
b 231386
16.7%
e 231008
16.6%
c 230943
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2311211
62.5%
Latin 1388277
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 232011
10.0%
8 231576
10.0%
5 231572
10.0%
1 231303
10.0%
0 231222
10.0%
2 231089
10.0%
7 230957
10.0%
9 230772
10.0%
3 230468
10.0%
4 230241
10.0%
Latin
ValueCountFrequency (%)
a 232033
16.7%
d 231486
16.7%
f 231421
16.7%
b 231386
16.7%
e 231008
16.6%
c 230943
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 232033
 
6.3%
6 232011
 
6.3%
8 231576
 
6.3%
5 231572
 
6.3%
d 231486
 
6.3%
f 231421
 
6.3%
b 231386
 
6.3%
1 231303
 
6.3%
0 231222
 
6.3%
2 231089
 
6.2%
Other values (6) 1384389
37.4%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
5
65374 
4
21951 
1
14546 
3
9718 
2
 
4020

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115609
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row5

Common Values

ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Length

2023-12-06T11:43:49.185976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T11:43:49.278341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring characters

ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 115609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

review_comment_title
Text

MISSING 

Distinct4477
Distinct (%)32.4%
Missing101808
Missing (%)88.1%
Memory size1.8 MiB
2023-12-06T11:43:49.582579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length12.187523
Min length1

Characters and Unicode

Total characters168200
Distinct characters125
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3063 ?
Unique (%)22.2%

Sample

1st rowPedido recebido corretame
2nd rowÓtimo
3rd rowok, muito profissional
4th rowÓtimo
5th rowTenho uma reclamação
ValueCountFrequency (%)
recomendo 2442
 
9.3%
produto 1541
 
5.8%
bom 1512
 
5.7%
muito 1036
 
3.9%
super 1033
 
3.9%
não 904
 
3.4%
ótimo 799
 
3.0%
excelente 763
 
2.9%
entrega 698
 
2.6%
recebi 435
 
1.7%
Other values (2076) 15191
57.6%
2023-12-06T11:43:50.024349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 20935
 
12.4%
e 17995
 
10.7%
14939
 
8.9%
r 9698
 
5.8%
t 9295
 
5.5%
a 8991
 
5.3%
m 8319
 
4.9%
d 8093
 
4.8%
i 7977
 
4.7%
n 7525
 
4.5%
Other values (115) 54433
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130612
77.7%
Uppercase Letter 18662
 
11.1%
Space Separator 14939
 
8.9%
Other Punctuation 2659
 
1.6%
Decimal Number 1226
 
0.7%
Other Symbol 48
 
< 0.1%
Dash Punctuation 19
 
< 0.1%
Modifier Symbol 13
 
< 0.1%
Math Symbol 8
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 20935
16.0%
e 17995
13.8%
r 9698
 
7.4%
t 9295
 
7.1%
a 8991
 
6.9%
m 8319
 
6.4%
d 8093
 
6.2%
i 7977
 
6.1%
n 7525
 
5.8%
c 5854
 
4.5%
Other values (31) 25930
19.9%
Uppercase Letter
ValueCountFrequency (%)
E 2511
13.5%
R 2031
10.9%
O 1666
 
8.9%
P 1580
 
8.5%
M 1441
 
7.7%
N 1172
 
6.3%
S 1035
 
5.5%
A 974
 
5.2%
Ó 924
 
5.0%
B 890
 
4.8%
Other values (26) 4438
23.8%
Other Punctuation
ValueCountFrequency (%)
! 1200
45.1%
. 782
29.4%
* 399
 
15.0%
, 152
 
5.7%
? 50
 
1.9%
/ 36
 
1.4%
% 21
 
0.8%
: 6
 
0.2%
; 4
 
0.2%
" 3
 
0.1%
Other values (4) 6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 461
37.6%
1 410
33.4%
5 80
 
6.5%
2 75
 
6.1%
8 46
 
3.8%
3 42
 
3.4%
4 39
 
3.2%
9 29
 
2.4%
7 23
 
1.9%
6 21
 
1.7%
Other Symbol
ValueCountFrequency (%)
👍 16
33.3%
😍 9
18.8%
👏 7
14.6%
🌟 6
 
12.5%
💥 5
 
10.4%
👎 1
 
2.1%
🤗 1
 
2.1%
🚚 1
 
2.1%
😀 1
 
2.1%
🔟 1
 
2.1%
Modifier Symbol
ValueCountFrequency (%)
´ 6
46.2%
🏻 3
23.1%
🏼 2
 
15.4%
🏽 2
 
15.4%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
= 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 6
85.7%
] 1
 
14.3%
Space Separator
ValueCountFrequency (%)
14939
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149275
88.7%
Common 18925
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20935
14.0%
e 17995
12.1%
r 9698
 
6.5%
t 9295
 
6.2%
a 8991
 
6.0%
m 8319
 
5.6%
d 8093
 
5.4%
i 7977
 
5.3%
n 7525
 
5.0%
c 5854
 
3.9%
Other values (68) 44593
29.9%
Common
ValueCountFrequency (%)
14939
78.9%
! 1200
 
6.3%
. 782
 
4.1%
0 461
 
2.4%
1 410
 
2.2%
* 399
 
2.1%
, 152
 
0.8%
5 80
 
0.4%
2 75
 
0.4%
? 50
 
0.3%
Other values (37) 377
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164629
97.9%
None 3561
 
2.1%
Emoticons 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20935
12.7%
e 17995
 
10.9%
14939
 
9.1%
r 9698
 
5.9%
t 9295
 
5.6%
a 8991
 
5.5%
m 8319
 
5.1%
d 8093
 
4.9%
i 7977
 
4.8%
n 7525
 
4.6%
Other values (75) 50862
30.9%
None
ValueCountFrequency (%)
ã 1086
30.5%
Ó 924
25.9%
á 356
 
10.0%
ç 342
 
9.6%
é 249
 
7.0%
ó 245
 
6.9%
à 71
 
2.0%
í 64
 
1.8%
ê 45
 
1.3%
É 29
 
0.8%
Other values (28) 150
 
4.2%
Emoticons
ValueCountFrequency (%)
😍 9
90.0%
😀 1
 
10.0%
Distinct35176
Distinct (%)71.9%
Missing66703
Missing (%)57.7%
Memory size1.8 MiB
2023-12-06T11:43:50.385092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length208
Median length158
Mean length70.205149
Min length1

Characters and Unicode

Total characters3433453
Distinct characters205
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28730 ?
Unique (%)58.7%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
5th rowSó achei ela pequena pra seis xícaras ,mais é um bom produto
ValueCountFrequency (%)
o 22075
 
3.8%
produto 20487
 
3.5%
e 19397
 
3.3%
a 14735
 
2.5%
de 14075
 
2.4%
do 12785
 
2.2%
não 12702
 
2.2%
que 10115
 
1.7%
prazo 9242
 
1.6%
muito 8930
 
1.5%
Other values (19329) 441610
75.3%
2023-12-06T11:43:50.889217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
543604
15.8%
o 338902
 
9.9%
e 329088
 
9.6%
a 272669
 
7.9%
r 193607
 
5.6%
i 158463
 
4.6%
t 156573
 
4.6%
d 145458
 
4.2%
n 133224
 
3.9%
s 129633
 
3.8%
Other values (195) 1032232
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2595479
75.6%
Space Separator 543604
 
15.8%
Uppercase Letter 162409
 
4.7%
Other Punctuation 93611
 
2.7%
Decimal Number 21346
 
0.6%
Control 13578
 
0.4%
Dash Punctuation 943
 
< 0.1%
Close Punctuation 718
 
< 0.1%
Open Punctuation 701
 
< 0.1%
Other Symbol 690
 
< 0.1%
Other values (5) 374
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
👏 241
34.9%
👍 86
 
12.5%
😍 74
 
10.7%
° 27
 
3.9%
😉 22
 
3.2%
😘 20
 
2.9%
😆 19
 
2.8%
😡 18
 
2.6%
😁 13
 
1.9%
👎 13
 
1.9%
Other values (51) 157
22.8%
Lowercase Letter
ValueCountFrequency (%)
o 338902
13.1%
e 329088
12.7%
a 272669
10.5%
r 193607
 
7.5%
i 158463
 
6.1%
t 156573
 
6.0%
d 145458
 
5.6%
n 133224
 
5.1%
s 129633
 
5.0%
m 124354
 
4.8%
Other values (40) 613508
23.6%
Uppercase Letter
ValueCountFrequency (%)
E 19186
11.8%
O 18074
11.1%
A 16813
10.4%
P 12154
 
7.5%
R 11806
 
7.3%
C 9540
 
5.9%
N 9239
 
5.7%
M 9162
 
5.6%
S 8085
 
5.0%
T 7584
 
4.7%
Other values (31) 40766
25.1%
Other Punctuation
ValueCountFrequency (%)
. 49261
52.6%
, 27288
29.2%
! 12446
 
13.3%
/ 1787
 
1.9%
? 1566
 
1.7%
" 410
 
0.4%
: 301
 
0.3%
; 222
 
0.2%
% 192
 
0.2%
* 72
 
0.1%
Other values (5) 66
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 4919
23.0%
1 4891
22.9%
2 4095
19.2%
3 1917
 
9.0%
4 1328
 
6.2%
5 1223
 
5.7%
8 921
 
4.3%
6 877
 
4.1%
7 717
 
3.4%
9 458
 
2.1%
Math Symbol
ValueCountFrequency (%)
+ 88
61.1%
= 27
 
18.8%
| 12
 
8.3%
< 9
 
6.2%
~ 3
 
2.1%
× 2
 
1.4%
> 2
 
1.4%
÷ 1
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
🏻 34
33.3%
´ 26
25.5%
🏼 15
14.7%
🏽 13
 
12.7%
^ 8
 
7.8%
🏾 4
 
3.9%
` 2
 
2.0%
Control
ValueCountFrequency (%)
6779
49.9%
6779
49.9%
20
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 715
99.6%
] 3
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 695
99.1%
[ 6
 
0.9%
Other Letter
ValueCountFrequency (%)
º 24
55.8%
ª 19
44.2%
Space Separator
ValueCountFrequency (%)
543604
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 943
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 77
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2757931
80.3%
Common 675522
 
19.7%

Most frequent character per script

Common
ValueCountFrequency (%)
543604
80.5%
. 49261
 
7.3%
, 27288
 
4.0%
! 12446
 
1.8%
6779
 
1.0%
6779
 
1.0%
0 4919
 
0.7%
1 4891
 
0.7%
2 4095
 
0.6%
3 1917
 
0.3%
Other values (102) 13543
 
2.0%
Latin
ValueCountFrequency (%)
o 338902
12.3%
e 329088
11.9%
a 272669
 
9.9%
r 193607
 
7.0%
i 158463
 
5.7%
t 156573
 
5.7%
d 145458
 
5.3%
n 133224
 
4.8%
s 129633
 
4.7%
m 124354
 
4.5%
Other values (83) 775960
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3371158
98.2%
None 62050
 
1.8%
Emoticons 245
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
543604
16.1%
o 338902
 
10.1%
e 329088
 
9.8%
a 272669
 
8.1%
r 193607
 
5.7%
i 158463
 
4.7%
t 156573
 
4.6%
d 145458
 
4.3%
n 133224
 
4.0%
s 129633
 
3.8%
Other values (85) 969937
28.8%
None
ValueCountFrequency (%)
ã 18253
29.4%
é 11197
18.0%
á 8923
14.4%
ç 7252
 
11.7%
ó 6189
 
10.0%
ê 1902
 
3.1%
í 1714
 
2.8%
Ó 1543
 
2.5%
õ 908
 
1.5%
ú 886
 
1.4%
Other values (71) 3283
 
5.3%
Emoticons
ValueCountFrequency (%)
😍 74
30.2%
😉 22
 
9.0%
😘 20
 
8.2%
😆 19
 
7.8%
😡 18
 
7.3%
😁 13
 
5.3%
😊 12
 
4.9%
😀 8
 
3.3%
😩 7
 
2.9%
😃 6
 
2.4%
Other values (19) 46
18.8%
Distinct632
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-10-15 00:00:00
Maximum2018-08-31 00:00:00
2023-12-06T11:43:51.012914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:51.094410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct96163
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-10-16 03:20:17
Maximum2018-10-29 12:27:35
2023-12-06T11:43:51.187937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:51.300459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:51.498491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length46
Median length30
Mean length14.875062
Min length3

Characters and Unicode

Total characters1719691
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowutilidades_domesticas
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 11847
 
10.2%
beleza_saude 9944
 
8.6%
esporte_lazer 8942
 
7.7%
moveis_decoracao 8743
 
7.6%
informatica_acessorios 8105
 
7.0%
utilidades_domesticas 7331
 
6.3%
relogios_presentes 6161
 
5.3%
telefonia 4692
 
4.1%
ferramentas_jardim 4558
 
3.9%
automotivo 4356
 
3.8%
Other values (61) 40930
35.4%
2023-12-06T11:43:51.832350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1609941
93.6%
Connector Punctuation 109454
 
6.4%
Decimal Number 296
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 208749
13.0%
a 206939
12.9%
s 171469
10.7%
o 170145
10.6%
i 114273
 
7.1%
r 110581
 
6.9%
t 82596
 
5.1%
c 81711
 
5.1%
m 77941
 
4.8%
n 58609
 
3.6%
Other values (16) 326928
20.3%
Connector Punctuation
ValueCountFrequency (%)
_ 109454
100.0%
Decimal Number
ValueCountFrequency (%)
2 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1609941
93.6%
Common 109750
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 208749
13.0%
a 206939
12.9%
s 171469
10.7%
o 170145
10.6%
i 114273
 
7.1%
r 110581
 
6.9%
t 82596
 
5.1%
c 81711
 
5.1%
m 77941
 
4.8%
n 58609
 
3.6%
Other values (16) 326928
20.3%
Common
ValueCountFrequency (%)
_ 109454
99.7%
2 296
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1719691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.766541
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:51.927070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.034187
Coefficient of variation (CV)0.20575966
Kurtosis0.14955291
Mean48.766541
Median Absolute Deviation (MAD)6
Skewness-0.90541969
Sum5637851
Variance100.68491
MonotonicityNot monotonic
2023-12-06T11:43:52.012933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8598
 
7.4%
60 8004
 
6.9%
56 6803
 
5.9%
58 6753
 
5.8%
57 6261
 
5.4%
55 5797
 
5.0%
54 5469
 
4.7%
53 4319
 
3.7%
52 4280
 
3.7%
49 3670
 
3.2%
Other values (56) 55655
48.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 14
 
< 0.1%
10 8
 
< 0.1%
11 11
 
< 0.1%
12 37
< 0.1%
13 26
< 0.1%
14 47
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
64 170
 
0.1%
63 1335
1.2%
62 160
 
0.1%
61 239
 
0.2%
Distinct2958
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.8082
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:52.106218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2120
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.41862
Coefficient of variation (CV)0.83025173
Kurtosis4.9272446
Mean785.8082
Median Absolute Deviation (MAD)295
Skewness2.011533
Sum90846500
Variance425650.05
MonotonicityNot monotonic
2023-12-06T11:43:52.195469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 708
 
0.6%
1893 664
 
0.6%
348 643
 
0.6%
492 590
 
0.5%
903 588
 
0.5%
245 576
 
0.5%
366 532
 
0.5%
236 513
 
0.4%
340 484
 
0.4%
919 436
 
0.4%
Other values (2948) 109875
95.0%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2053733
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:52.281046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.717771
Coefficient of variation (CV)0.7789026
Kurtosis4.8372556
Mean2.2053733
Median Absolute Deviation (MAD)0
Skewness1.910868
Sum254961
Variance2.9507372
MonotonicityNot monotonic
2023-12-06T11:43:52.350333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
6 3905
 
3.4%
7 1552
 
1.3%
8 771
 
0.7%
10 353
 
0.3%
9 309
 
0.3%
Other values (9) 199
 
0.2%
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
6 3905
 
3.4%
7 1552
 
1.3%
8 771
 
0.7%
9 309
 
0.3%
10 353
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 353
0.3%

product_weight_g
Real number (ℝ)

HIGH CORRELATION 

Distinct2197
Distinct (%)1.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2113.9077
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:52.453635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3781.7549
Coefficient of variation (CV)1.7889877
Kurtosis16.034544
Mean2113.9077
Median Absolute Deviation (MAD)500
Skewness3.5806498
Sum2.4438464 × 108
Variance14301670
MonotonicityNot monotonic
2023-12-06T11:43:52.577155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6815
 
5.9%
150 5329
 
4.6%
250 4671
 
4.0%
300 4313
 
3.7%
100 3571
 
3.1%
400 3481
 
3.0%
350 3211
 
2.8%
500 2787
 
2.4%
600 2778
 
2.4%
700 2095
 
1.8%
Other values (2187) 76557
66.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 985
0.9%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 296
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.307903
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:52.709008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.211108
Coefficient of variation (CV)0.53488058
Kurtosis3.6631229
Mean30.307903
Median Absolute Deviation (MAD)8
Skewness1.7425099
Sum3503836
Variance262.80004
MonotonicityNot monotonic
2023-12-06T11:43:52.829653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 17712
 
15.3%
20 10603
 
9.2%
30 7814
 
6.8%
17 6126
 
5.3%
18 5844
 
5.1%
19 4845
 
4.2%
25 4768
 
4.1%
40 4238
 
3.7%
22 3935
 
3.4%
50 3091
 
2.7%
Other values (89) 46632
40.3%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 137
 
0.1%
15 212
 
0.2%
16 17712
15.3%
ValueCountFrequency (%)
105 331
0.3%
104 30
 
< 0.1%
103 45
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%
100 424
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.638477
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:52.957753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.47357
Coefficient of variation (CV)0.80978385
Kurtosis7.2875078
Mean16.638477
Median Absolute Deviation (MAD)6
Skewness2.2431211
Sum1923541
Variance181.53708
MonotonicityNot monotonic
2023-12-06T11:43:53.081841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10208
 
8.8%
20 6796
 
5.9%
15 6784
 
5.9%
11 6309
 
5.5%
12 6196
 
5.4%
2 5097
 
4.4%
4 4778
 
4.1%
8 4773
 
4.1%
16 4661
 
4.0%
5 4636
 
4.0%
Other values (92) 55370
47.9%
ValueCountFrequency (%)
2 5097
4.4%
3 2770
 
2.4%
4 4778
4.1%
5 4636
4.0%
6 3511
 
3.0%
7 4217
3.6%
8 4773
4.1%
9 3369
 
2.9%
10 10208
8.8%
11 6309
5.5%
ValueCountFrequency (%)
105 137
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 42
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.113167
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:53.205333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.755083
Coefficient of variation (CV)0.50858817
Kurtosis4.5531896
Mean23.113167
Median Absolute Deviation (MAD)6
Skewness1.7072171
Sum2672067
Variance138.18198
MonotonicityNot monotonic
2023-12-06T11:43:53.314517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12447
 
10.8%
11 10662
 
9.2%
15 8964
 
7.8%
16 8677
 
7.5%
30 7880
 
6.8%
12 5590
 
4.8%
13 5412
 
4.7%
14 4753
 
4.1%
18 4122
 
3.6%
40 4042
 
3.5%
Other values (85) 43059
37.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 10662
9.2%
12 5590
4.8%
13 5412
4.7%
14 4753
4.1%
15 8964
7.8%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%

seller_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct2210
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24515.714
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:53.405104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2969
Q16429
median13660
Q328605
95-th percentile88316
Maximum99730
Range98729
Interquartile range (IQR)22176

Descriptive statistics

Standard deviation27636.641
Coefficient of variation (CV)1.1273031
Kurtosis0.9066511
Mean24515.714
Median Absolute Deviation (MAD)8132
Skewness1.548043
Sum2.8342372 × 109
Variance7.6378392 × 108
MonotonicityNot monotonic
2023-12-06T11:43:53.500305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14940 8227
 
7.1%
5849 2126
 
1.8%
15025 2090
 
1.8%
9015 1891
 
1.6%
13405 1667
 
1.4%
8577 1546
 
1.3%
4782 1536
 
1.3%
3204 1465
 
1.3%
4160 1261
 
1.1%
13232 1242
 
1.1%
Other values (2200) 92558
80.1%
ValueCountFrequency (%)
1001 22
 
< 0.1%
1021 41
 
< 0.1%
1022 5
 
< 0.1%
1023 5
 
< 0.1%
1026 302
0.3%
1031 122
0.1%
1035 18
 
< 0.1%
1039 1
 
< 0.1%
1040 21
 
< 0.1%
1041 2
 
< 0.1%
ValueCountFrequency (%)
99730 12
 
< 0.1%
99700 2
 
< 0.1%
99670 1
 
< 0.1%
99500 61
0.1%
99300 2
 
< 0.1%
98975 19
 
< 0.1%
98920 1
 
< 0.1%
98910 14
 
< 0.1%
98803 65
0.1%
98780 4
 
< 0.1%
Distinct604
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:53.756414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length10.102155
Min length2

Characters and Unicode

Total characters1167900
Distinct characters63
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.1%

Sample

1st rowMaua
2nd rowMaua
3rd rowMaua
4th rowMaua
5th rowMaua
ValueCountFrequency (%)
sao 35785
 
18.0%
paulo 29092
 
14.6%
ibitinga 8227
 
4.1%
rio 5802
 
2.9%
preto 5428
 
2.7%
do 5420
 
2.7%
jose 4035
 
2.0%
de 3991
 
2.0%
santo 3250
 
1.6%
andre 3146
 
1.6%
Other values (634) 94626
47.6%
2023-12-06T11:43:54.138869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 187194
16.0%
o 141616
12.1%
i 86927
 
7.4%
83253
 
7.1%
r 66040
 
5.7%
e 62563
 
5.4%
u 60154
 
5.2%
l 50605
 
4.3%
S 46454
 
4.0%
P 46176
 
4.0%
Other values (53) 336918
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 883933
75.7%
Uppercase Letter 199507
 
17.1%
Space Separator 83253
 
7.1%
Other Punctuation 606
 
0.1%
Modifier Symbol 365
 
< 0.1%
Dash Punctuation 164
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Close Punctuation 31
 
< 0.1%
Decimal Number 8
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 187194
21.2%
o 141616
16.0%
i 86927
9.8%
r 66040
 
7.5%
e 62563
 
7.1%
u 60154
 
6.8%
l 50605
 
5.7%
t 44895
 
5.1%
n 44171
 
5.0%
s 27876
 
3.2%
Other values (14) 111892
12.7%
Uppercase Letter
ValueCountFrequency (%)
S 46454
23.3%
P 46176
23.1%
C 13499
 
6.8%
D 13382
 
6.7%
I 13117
 
6.6%
J 10123
 
5.1%
R 10071
 
5.0%
B 9450
 
4.7%
A 7153
 
3.6%
G 6464
 
3.2%
Other values (12) 23618
11.8%
Other Punctuation
ValueCountFrequency (%)
' 345
56.9%
/ 138
 
22.8%
. 74
 
12.2%
@ 37
 
6.1%
\ 6
 
1.0%
, 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
4 2
25.0%
2 2
25.0%
5 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
83253
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1083440
92.8%
Common 84458
 
7.2%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 187194
17.3%
o 141616
13.1%
i 86927
 
8.0%
r 66040
 
6.1%
e 62563
 
5.8%
u 60154
 
5.6%
l 50605
 
4.7%
S 46454
 
4.3%
P 46176
 
4.3%
t 44895
 
4.1%
Other values (36) 290816
26.8%
Common
ValueCountFrequency (%)
83253
98.6%
´ 365
 
0.4%
' 345
 
0.4%
- 164
 
0.2%
/ 138
 
0.2%
. 74
 
0.1%
@ 37
 
< 0.1%
( 31
 
< 0.1%
) 31
 
< 0.1%
\ 6
 
< 0.1%
Other values (6) 14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167533
> 99.9%
None 365
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 187194
16.0%
o 141616
12.1%
i 86927
 
7.4%
83253
 
7.1%
r 66040
 
5.7%
e 62563
 
5.4%
u 60154
 
5.2%
l 50605
 
4.3%
S 46454
 
4.0%
P 46176
 
4.0%
Other values (51) 336551
28.8%
None
ValueCountFrequency (%)
´ 365
100.0%
Diacriticals
ValueCountFrequency (%)
̃ 2
100.0%

seller_state
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
82417 
MG
9014 
PR
8964 
RJ
 
4906
SC
 
4221
Other values (18)
 
6087

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231218
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 82417
71.3%
MG 9014
 
7.8%
PR 8964
 
7.8%
RJ 4906
 
4.2%
SC 4221
 
3.7%
RS 2224
 
1.9%
DF 937
 
0.8%
BA 698
 
0.6%
GO 537
 
0.5%
PE 461
 
0.4%
Other values (13) 1230
 
1.1%

Length

2023-12-06T11:43:54.236660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 82417
71.3%
mg 9014
 
7.8%
pr 8964
 
7.8%
rj 4906
 
4.2%
sc 4221
 
3.7%
rs 2224
 
1.9%
df 937
 
0.8%
ba 698
 
0.6%
go 537
 
0.5%
pe 461
 
0.4%
Other values (13) 1230
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 91902
39.7%
S 89305
38.6%
R 16164
 
7.0%
M 9626
 
4.2%
G 9551
 
4.1%
J 4906
 
2.1%
C 4325
 
1.9%
A 1113
 
0.5%
E 948
 
0.4%
D 937
 
0.4%
Other values (6) 2441
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231218
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 91902
39.7%
S 89305
38.6%
R 16164
 
7.0%
M 9626
 
4.2%
G 9551
 
4.1%
J 4906
 
2.1%
C 4325
 
1.9%
A 1113
 
0.5%
E 948
 
0.4%
D 937
 
0.4%
Other values (6) 2441
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 231218
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 91902
39.7%
S 89305
38.6%
R 16164
 
7.0%
M 9626
 
4.2%
G 9551
 
4.1%
J 4906
 
2.1%
C 4325
 
1.9%
A 1113
 
0.5%
E 948
 
0.4%
D 937
 
0.4%
Other values (6) 2441
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 91902
39.7%
S 89305
38.6%
R 16164
 
7.0%
M 9626
 
4.2%
G 9551
 
4.1%
J 4906
 
2.1%
C 4325
 
1.9%
A 1113
 
0.5%
E 948
 
0.4%
D 937
 
0.4%
Other values (6) 2441
 
1.1%
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:54.470555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.989949
Min length3

Characters and Unicode

Total characters1501755
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHousewares
2nd rowHousewares
3rd rowHousewares
4th rowHousewares
5th rowHousewares
ValueCountFrequency (%)
bed 11847
 
5.5%
table 11847
 
5.5%
bath 11847
 
5.5%
furniture 11502
 
5.3%
accessories 11423
 
5.3%
health 9944
 
4.6%
beauty 9944
 
4.6%
sports 8942
 
4.1%
leisure 8942
 
4.1%
decor 8743
 
4.0%
Other values (99) 111939
51.6%
2023-12-06T11:43:54.846949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 181025
 
12.1%
s 121642
 
8.1%
o 109159
 
7.3%
r 104416
 
7.0%
t 104286
 
6.9%
101311
 
6.7%
a 81879
 
5.5%
u 77400
 
5.2%
i 60865
 
4.1%
c 53224
 
3.5%
Other values (34) 506548
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1183524
78.8%
Uppercase Letter 216624
 
14.4%
Space Separator 101311
 
6.7%
Decimal Number 296
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 181025
15.3%
s 121642
10.3%
o 109159
9.2%
r 104416
8.8%
t 104286
8.8%
a 81879
 
6.9%
u 77400
 
6.5%
i 60865
 
5.1%
c 53224
 
4.5%
n 48622
 
4.1%
Other values (13) 241006
20.4%
Uppercase Letter
ValueCountFrequency (%)
B 40475
18.7%
T 27868
12.9%
A 19718
9.1%
H 19617
9.1%
S 19408
9.0%
C 18732
8.6%
F 15461
 
7.1%
G 13004
 
6.0%
L 11240
 
5.2%
D 9837
 
4.5%
Other values (9) 21264
9.8%
Space Separator
ValueCountFrequency (%)
101311
100.0%
Decimal Number
ValueCountFrequency (%)
2 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1400148
93.2%
Common 101607
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 181025
 
12.9%
s 121642
 
8.7%
o 109159
 
7.8%
r 104416
 
7.5%
t 104286
 
7.4%
a 81879
 
5.8%
u 77400
 
5.5%
i 60865
 
4.3%
c 53224
 
3.8%
n 48622
 
3.5%
Other values (32) 457630
32.7%
Common
ValueCountFrequency (%)
101311
99.7%
2 296
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1501755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 181025
 
12.1%
s 121642
 
8.1%
o 109159
 
7.3%
r 104416
 
7.0%
t 104286
 
6.9%
101311
 
6.7%
a 81879
 
5.5%
u 77400
 
5.2%
i 60865
 
4.1%
c 53224
 
3.5%
Other values (34) 506548
33.7%

review_response_time
Real number (ℝ)

SKEWED  ZEROS 

Distinct210
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5819443
Minimum0
Maximum518
Zeros28066
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:54.952840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile6
Maximum518
Range518
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.7851142
Coefficient of variation (CV)3.7898239
Kurtosis829.09885
Mean2.5819443
Median Absolute Deviation (MAD)1
Skewness24.09892
Sum298496
Variance95.74846
MonotonicityNot monotonic
2023-12-06T11:43:55.464232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 36120
31.2%
0 28066
24.3%
2 18555
16.0%
3 16464
14.2%
4 5350
 
4.6%
5 3229
 
2.8%
6 2050
 
1.8%
7 1060
 
0.9%
8 744
 
0.6%
9 533
 
0.5%
Other values (200) 3438
 
3.0%
ValueCountFrequency (%)
0 28066
24.3%
1 36120
31.2%
2 18555
16.0%
3 16464
14.2%
4 5350
 
4.6%
5 3229
 
2.8%
6 2050
 
1.8%
7 1060
 
0.9%
8 744
 
0.6%
9 533
 
0.5%
ValueCountFrequency (%)
518 1
 
< 0.1%
512 1
 
< 0.1%
508 3
< 0.1%
471 1
 
< 0.1%
446 1
 
< 0.1%
433 1
 
< 0.1%
412 1
 
< 0.1%
411 1
 
< 0.1%
383 1
 
< 0.1%
367 1
 
< 0.1%

time_to_delivery
Real number (ℝ)

MISSING 

Distinct143
Distinct (%)0.1%
Missing2400
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean11.976866
Minimum0
Maximum208
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:55.562353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median10
Q315
95-th percentile28
Maximum208
Range208
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.3622377
Coefficient of variation (CV)0.78169346
Kurtosis39.237666
Mean11.976866
Median Absolute Deviation (MAD)4
Skewness3.7914596
Sum1355889
Variance87.651495
MonotonicityNot monotonic
2023-12-06T11:43:55.646535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 8968
 
7.8%
6 8011
 
6.9%
8 7961
 
6.9%
9 7090
 
6.1%
5 6874
 
5.9%
10 6710
 
5.8%
11 6099
 
5.3%
4 5712
 
4.9%
12 5477
 
4.7%
13 5140
 
4.4%
Other values (133) 45167
39.1%
ValueCountFrequency (%)
0 19
 
< 0.1%
1 1856
 
1.6%
2 3777
3.3%
3 4512
3.9%
4 5712
4.9%
5 6874
5.9%
6 8011
6.9%
7 8968
7.8%
8 7961
6.9%
9 7090
6.1%
ValueCountFrequency (%)
208 1
 
< 0.1%
195 1
 
< 0.1%
194 3
< 0.1%
191 1
 
< 0.1%
189 1
 
< 0.1%
188 1
 
< 0.1%
187 3
< 0.1%
186 1
 
< 0.1%
182 2
< 0.1%
181 1
 
< 0.1%

delivery_against_estimated
Real number (ℝ)

MISSING  ZEROS 

Distinct194
Distinct (%)0.2%
Missing2400
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean-12.092351
Minimum-147
Maximum188
Zeros1468
Zeros (%)1.3%
Negative104494
Negative (%)90.4%
Memory size1.8 MiB
2023-12-06T11:43:55.758832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-147
5-th percentile-27
Q1-17
median-13
Q3-7
95-th percentile3
Maximum188
Range335
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.090815
Coefficient of variation (CV)-0.83447915
Kurtosis27.60215
Mean-12.092351
Median Absolute Deviation (MAD)5
Skewness1.8526615
Sum-1368963
Variance101.82455
MonotonicityNot monotonic
2023-12-06T11:43:55.854797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 8425
 
7.3%
-13 6967
 
6.0%
-15 6291
 
5.4%
-7 5669
 
4.9%
-8 5605
 
4.8%
-9 5442
 
4.7%
-10 5434
 
4.7%
-11 5409
 
4.7%
-12 5333
 
4.6%
-16 4628
 
4.0%
Other values (184) 54006
46.7%
ValueCountFrequency (%)
-147 2
< 0.1%
-140 1
 
< 0.1%
-135 1
 
< 0.1%
-124 2
< 0.1%
-109 1
 
< 0.1%
-84 1
 
< 0.1%
-83 1
 
< 0.1%
-78 4
< 0.1%
-77 1
 
< 0.1%
-76 1
 
< 0.1%
ValueCountFrequency (%)
188 1
 
< 0.1%
175 1
 
< 0.1%
167 1
 
< 0.1%
166 1
 
< 0.1%
165 1
 
< 0.1%
162 1
 
< 0.1%
161 5
< 0.1%
159 1
 
< 0.1%
155 3
< 0.1%
153 2
 
< 0.1%

product_volume_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct4480
Distinct (%)3.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15395.611
Minimum168
Maximum296208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:55.981742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile816
Q12860
median6650
Q318576
95-th percentile58500
Maximum296208
Range296040
Interquartile range (IQR)15716

Descriptive statistics

Standard deviation23618.937
Coefficient of variation (CV)1.5341344
Kurtosis25.356306
Mean15395.611
Median Absolute Deviation (MAD)4778
Skewness4.0676347
Sum1.7798558 × 109
Variance5.5785418 × 108
MonotonicityNot monotonic
2023-12-06T11:43:56.098866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 2790
 
2.4%
352 1801
 
1.6%
19800 1305
 
1.1%
4096 1183
 
1.0%
816 1110
 
1.0%
640 1103
 
1.0%
27000 994
 
0.9%
2560 967
 
0.8%
4800 955
 
0.8%
4500 935
 
0.8%
Other values (4470) 102465
88.6%
ValueCountFrequency (%)
168 1
 
< 0.1%
288 1
 
< 0.1%
352 1801
1.6%
374 4
 
< 0.1%
378 1
 
< 0.1%
384 10
 
< 0.1%
396 12
 
< 0.1%
408 3
 
< 0.1%
416 5
 
< 0.1%
418 4
 
< 0.1%
ValueCountFrequency (%)
296208 1
 
< 0.1%
294000 4
< 0.1%
293706 1
 
< 0.1%
288000 7
< 0.1%
287980 4
< 0.1%
285138 1
 
< 0.1%
282750 3
< 0.1%
281232 1
 
< 0.1%
277550 2
 
< 0.1%
274625 1
 
< 0.1%

order_purchase_year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2018
62721 
2017
52507 
2016
 
381

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters462436
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2018 62721
54.3%
2017 52507
45.4%
2016 381
 
0.3%

Length

2023-12-06T11:43:56.194576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T11:43:56.265506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 62721
54.3%
2017 52507
45.4%
2016 381
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 115609
25.0%
0 115609
25.0%
1 115609
25.0%
8 62721
13.6%
7 52507
11.4%
6 381
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 462436
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 115609
25.0%
0 115609
25.0%
1 115609
25.0%
8 62721
13.6%
7 52507
11.4%
6 381
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 462436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 115609
25.0%
0 115609
25.0%
1 115609
25.0%
8 62721
13.6%
7 52507
11.4%
6 381
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 115609
25.0%
0 115609
25.0%
1 115609
25.0%
8 62721
13.6%
7 52507
11.4%
6 381
 
0.1%

order_purchase_year_month
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2017-11
8881 
2018-03
8380 
2018-01
8304 
2018-05
8127 
2018-04
8123 
Other values (19)
73794 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters809263
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2017-10
2nd row2017-10
3rd row2017-10
4th row2017-08
5th row2017-08

Common Values

ValueCountFrequency (%)
2017-11 8881
 
7.7%
2018-03 8380
 
7.2%
2018-01 8304
 
7.2%
2018-05 8127
 
7.0%
2018-04 8123
 
7.0%
2018-02 7817
 
6.8%
2018-08 7396
 
6.4%
2018-06 7331
 
6.3%
2018-07 7242
 
6.3%
2017-12 6371
 
5.5%
Other values (14) 37637
32.6%

Length

2023-12-06T11:43:56.342138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11 8881
 
7.7%
2018-03 8380
 
7.2%
2018-01 8304
 
7.2%
2018-05 8127
 
7.0%
2018-04 8123
 
7.0%
2018-02 7817
 
6.8%
2018-08 7396
 
6.4%
2018-06 7331
 
6.3%
2018-07 7242
 
6.3%
2017-12 6371
 
5.5%
Other values (14) 37637
32.6%

Most occurring characters

ValueCountFrequency (%)
0 215965
26.7%
1 154913
19.1%
2 131797
16.3%
- 115609
14.3%
8 75200
 
9.3%
7 64522
 
8.0%
5 12427
 
1.5%
3 11489
 
1.4%
6 11440
 
1.4%
4 10883
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 693654
85.7%
Dash Punctuation 115609
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 215965
31.1%
1 154913
22.3%
2 131797
19.0%
8 75200
 
10.8%
7 64522
 
9.3%
5 12427
 
1.8%
3 11489
 
1.7%
6 11440
 
1.6%
4 10883
 
1.6%
9 5018
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 115609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 809263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 215965
26.7%
1 154913
19.1%
2 131797
16.3%
- 115609
14.3%
8 75200
 
9.3%
7 64522
 
8.0%
5 12427
 
1.5%
3 11489
 
1.4%
6 11440
 
1.4%
4 10883
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 809263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 215965
26.7%
1 154913
19.1%
2 131797
16.3%
- 115609
14.3%
8 75200
 
9.3%
7 64522
 
8.0%
5 12427
 
1.5%
3 11489
 
1.4%
6 11440
 
1.4%
4 10883
 
1.3%

order_purchase_hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.76443
Minimum0
Maximum23
Zeros2835
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-12-06T11:43:56.439491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median15
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.321909
Coefficient of variation (CV)0.36045475
Kurtosis0.19481186
Mean14.76443
Median Absolute Deviation (MAD)4
Skewness-0.60132366
Sum1706901
Variance28.322715
MonotonicityNot monotonic
2023-12-06T11:43:56.513754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16 7835
 
6.8%
14 7776
 
6.7%
11 7636
 
6.6%
13 7569
 
6.5%
15 7497
 
6.5%
10 7200
 
6.2%
17 7138
 
6.2%
20 7127
 
6.2%
21 7105
 
6.1%
12 7056
 
6.1%
Other values (14) 41670
36.0%
ValueCountFrequency (%)
0 2835
2.5%
1 1310
 
1.1%
2 594
 
0.5%
3 311
 
0.3%
4 251
 
0.2%
5 213
 
0.2%
6 561
 
0.5%
7 1396
 
1.2%
8 3469
3.0%
9 5513
4.8%
ValueCountFrequency (%)
23 4811
4.2%
22 6810
5.9%
21 7105
6.1%
20 7127
6.2%
19 6858
5.9%
18 6738
5.8%
17 7138
6.2%
16 7835
6.8%
15 7497
6.5%
14 7776
6.7%

order_purchase_month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Aug
12479 
May
12427 
Jul
12015 
Mar
11489 
Jun
11059 
Other values (7)
56140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters346827
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOct
2nd rowOct
3rd rowOct
4th rowAug
5th rowAug

Common Values

ValueCountFrequency (%)
Aug 12479
10.8%
May 12427
10.7%
Jul 12015
10.4%
Mar 11489
9.9%
Jun 11059
9.6%
Apr 10883
9.4%
Feb 9816
8.5%
Jan 9302
8.0%
Nov 8881
7.7%
Dec 6372
5.5%
Other values (2) 10886
9.4%

Length

2023-12-06T11:43:56.586457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug 12479
10.8%
may 12427
10.7%
jul 12015
10.4%
mar 11489
9.9%
jun 11059
9.6%
apr 10883
9.4%
feb 9816
8.5%
jan 9302
8.0%
nov 8881
7.7%
dec 6372
5.5%
Other values (2) 10886
9.4%

Most occurring characters

ValueCountFrequency (%)
u 35553
 
10.3%
a 33218
 
9.6%
J 32376
 
9.3%
M 23916
 
6.9%
A 23362
 
6.7%
r 22372
 
6.5%
e 21206
 
6.1%
n 20361
 
5.9%
p 15901
 
4.6%
g 12479
 
3.6%
Other values (12) 106083
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231218
66.7%
Uppercase Letter 115609
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 35553
15.4%
a 33218
14.4%
r 22372
9.7%
e 21206
9.2%
n 20361
8.8%
p 15901
6.9%
g 12479
 
5.4%
y 12427
 
5.4%
c 12240
 
5.3%
l 12015
 
5.2%
Other values (4) 33446
14.5%
Uppercase Letter
ValueCountFrequency (%)
J 32376
28.0%
M 23916
20.7%
A 23362
20.2%
F 9816
 
8.5%
N 8881
 
7.7%
D 6372
 
5.5%
O 5868
 
5.1%
S 5018
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 346827
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 35553
 
10.3%
a 33218
 
9.6%
J 32376
 
9.3%
M 23916
 
6.9%
A 23362
 
6.7%
r 22372
 
6.5%
e 21206
 
6.1%
n 20361
 
5.9%
p 15901
 
4.6%
g 12479
 
3.6%
Other values (12) 106083
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 35553
 
10.3%
a 33218
 
9.6%
J 32376
 
9.3%
M 23916
 
6.9%
A 23362
 
6.7%
r 22372
 
6.5%
e 21206
 
6.1%
n 20361
 
5.9%
p 15901
 
4.6%
g 12479
 
3.6%
Other values (12) 106083
30.6%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Mon
18769 
Tue
18768 
Wed
18033 
Thu
17301 
Fri
16532 
Other values (2)
26206 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters346827
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMon
2nd rowMon
3rd rowMon
4th rowTue
5th rowWed

Common Values

ValueCountFrequency (%)
Mon 18769
16.2%
Tue 18768
16.2%
Wed 18033
15.6%
Thu 17301
15.0%
Fri 16532
14.3%
Sun 13722
11.9%
Sat 12484
10.8%

Length

2023-12-06T11:43:56.660039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-06T11:43:56.746250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mon 18769
16.2%
tue 18768
16.2%
wed 18033
15.6%
thu 17301
15.0%
fri 16532
14.3%
sun 13722
11.9%
sat 12484
10.8%

Most occurring characters

ValueCountFrequency (%)
u 49791
14.4%
e 36801
10.6%
T 36069
10.4%
n 32491
9.4%
S 26206
 
7.6%
M 18769
 
5.4%
o 18769
 
5.4%
W 18033
 
5.2%
d 18033
 
5.2%
h 17301
 
5.0%
Other values (5) 74564
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231218
66.7%
Uppercase Letter 115609
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 49791
21.5%
e 36801
15.9%
n 32491
14.1%
o 18769
 
8.1%
d 18033
 
7.8%
h 17301
 
7.5%
r 16532
 
7.1%
i 16532
 
7.1%
a 12484
 
5.4%
t 12484
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
T 36069
31.2%
S 26206
22.7%
M 18769
16.2%
W 18033
15.6%
F 16532
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 346827
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 49791
14.4%
e 36801
10.6%
T 36069
10.4%
n 32491
9.4%
S 26206
 
7.6%
M 18769
 
5.4%
o 18769
 
5.4%
W 18033
 
5.2%
d 18033
 
5.2%
h 17301
 
5.0%
Other values (5) 74564
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 49791
14.4%
e 36801
10.6%
T 36069
10.4%
n 32491
9.4%
S 26206
 
7.6%
M 18769
 
5.4%
o 18769
 
5.4%
W 18033
 
5.2%
d 18033
 
5.2%
h 17301
 
5.0%
Other values (5) 74564
21.5%

Interactions

2023-12-06T11:43:36.258309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:56.824499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.756516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.938556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.999302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.905061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.752866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.866492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.929876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:13.137579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:15.398170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:17.352731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:19.316896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:21.471673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:23.459395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:25.342348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:27.272017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.502948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:31.562711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.918422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:36.373124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:56.933711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.875035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:01.063786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:03.112761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.995685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.864577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-12-06T11:43:04.060126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:05.923953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.005683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.007621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.004019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.458055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.502028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.448174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:20.573670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:22.583867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.516710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.411042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:28.694391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:30.551308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:32.809346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.268016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:37.645006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:57.973493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.129508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.109951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.148237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.017061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.106971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.128312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.105052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.558517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.599871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.543404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:20.659157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:22.675165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.596960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.501981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:28.779510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:30.660249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:32.920563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.397446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:37.746165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.062430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.228914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.206339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.258088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.108881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.224103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.248632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.196552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.656113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.692463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.639348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:20.744756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:22.761189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.681529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.591933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:28.869701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:30.775393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.036507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.500284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:37.847193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.158797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.329491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.294798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.356109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.193373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.320178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.349893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.351214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.761359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.780573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.735974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:20.832539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:22.853047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.766071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.675465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:28.961499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:30.887616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.158957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.603534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:37.945246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.253334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.425014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.377519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.452735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.279964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.410485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.461609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.451638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.855797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.870914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.833965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:20.921664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:22.942408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.852886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.764648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.045759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:30.993155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.297281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.710577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:38.043958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.340216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.521060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.476169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.545284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.382329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.500633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.559889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.539669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:14.954978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:16.967540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:18.929758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:21.037434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:23.035103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:24.945077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.851181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.140118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:31.110679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.433938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.813488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:38.142263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.436831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.629916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.559447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.634121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.475581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.591232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.659161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.833814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:15.058414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:17.057558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:19.024266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:21.138581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:23.137154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:25.050076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:26.946686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.225745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:31.222562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.560716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:35.916013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:38.247046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.547791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.735810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.804231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.729358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.575173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.694393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.753319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:12.939340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:15.184484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:17.156124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:19.129019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:21.241922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:23.241115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:25.145123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:27.047529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.323083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:31.359695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.690540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:36.027674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:38.350643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:42:58.659265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:00.838583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:02.908935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:04.816768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:06.662139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:08.782409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:10.842506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:13.035086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:15.312041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:17.262926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:19.224532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:21.346945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:23.350922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:25.245320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:27.160889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:29.413212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:31.464929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:33.802078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-06T11:43:36.137538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-12-06T11:43:56.888248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
customer_statecustomer_zip_code_prefixdelivery_against_estimatedfreight_valueorder_item_idorder_purchase_dayofweekorder_purchase_hourorder_purchase_monthorder_purchase_yearorder_purchase_year_monthorder_statuspayment_installmentspayment_sequentialpayment_typepayment_valuepriceproduct_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_volume_cmproduct_weight_gproduct_width_cmreview_response_timereview_scoreseller_stateseller_zip_code_prefixtime_to_delivery
customer_state1.000-0.7200.103-0.4600.0170.014-0.0040.0200.0430.0400.027-0.0720.0020.039-0.096-0.069-0.030-0.0040.005-0.010-0.0190.004-0.0180.010-0.0210.0490.054-0.045-0.400
customer_zip_code_prefix-0.7201.000-0.1330.466-0.0090.0090.0130.0200.0370.0290.0230.069-0.0070.0330.1060.0700.0310.0190.0090.0160.0260.0140.026-0.0020.0230.0420.0660.0600.427
delivery_against_estimated0.103-0.1331.000-0.146-0.0360.0200.0290.0830.1480.1290.000-0.059-0.0070.026-0.063-0.0290.009-0.013-0.0030.0130.002-0.010-0.009-0.014-0.0560.0960.050-0.1390.307
freight_value-0.4600.466-0.1461.000-0.0560.0080.0070.0170.0280.0250.0160.1900.0160.0100.4240.4350.1170.2840.2820.0330.0100.3690.4480.2730.0170.0150.0480.2560.420
order_item_id0.017-0.009-0.036-0.0561.0000.008-0.0130.0090.0050.0140.0030.060-0.0070.0210.257-0.114-0.0320.0190.008-0.022-0.0640.0150.001-0.0030.0180.0410.000-0.012-0.022
order_purchase_dayofweek0.0140.0090.0200.0080.0081.0000.0020.0470.0250.0510.007-0.0160.0040.035-0.002-0.0030.0000.0030.0020.007-0.0020.0020.000-0.0020.0120.0120.0100.001-0.040
order_purchase_hour-0.0040.0130.0290.007-0.0130.0021.0000.0160.0190.0180.0090.0300.0120.043-0.0060.008-0.0090.0020.010-0.0000.0040.0100.0160.012-0.0040.0130.011-0.004-0.007
order_purchase_month0.0200.0200.0830.0170.0090.0470.0161.0000.4621.0000.0250.0220.0100.0350.0080.003-0.0160.0160.010-0.004-0.0000.0170.0210.013-0.0130.0560.0370.0040.047
order_purchase_year0.0430.0370.1480.0280.0050.0250.0190.4621.0001.0000.108-0.042-0.0340.0420.0220.0050.053-0.014-0.0670.019-0.022-0.056-0.050-0.058-0.0100.0190.084-0.038-0.093
order_purchase_year_month0.0400.0290.1290.0250.0140.0510.0181.0001.0001.0000.072-0.034-0.0390.0590.0270.0080.055-0.008-0.0800.030-0.009-0.060-0.055-0.061-0.0270.0630.040-0.038-0.164
order_status0.0270.0230.0000.0160.0030.0070.0090.0250.1080.0721.0000.0010.0020.0070.0020.0130.0090.0100.004-0.0060.0010.0090.0070.005-0.0170.1310.0290.005-0.003
payment_installments-0.0720.069-0.0590.1900.060-0.0160.0300.022-0.042-0.0340.0011.000-0.1770.2730.3960.3170.0330.1060.1090.016-0.0030.1480.1980.1250.0050.0280.0330.0660.039
payment_sequential0.002-0.007-0.0070.016-0.0070.0040.0120.010-0.034-0.0390.002-0.1771.0000.229-0.214-0.005-0.0130.0110.032-0.004-0.0040.0270.0280.026-0.0140.0120.0160.0060.009
payment_type0.0390.0330.0260.0100.0210.0350.0430.0350.0420.0590.0070.2730.2291.000-0.0930.033-0.0100.0090.020-0.003-0.0040.0220.0260.025-0.0170.0090.0210.003-0.072
payment_value-0.0960.106-0.0630.4240.257-0.002-0.0060.0080.0220.0270.0020.396-0.214-0.0931.0000.7900.1690.3060.2310.024-0.0120.3510.4520.2340.0240.0280.0370.1630.123
price-0.0690.070-0.0290.435-0.114-0.0030.0080.0030.0050.0080.0130.317-0.0050.0330.7901.0000.2110.3280.2680.0420.0280.3910.5150.2730.0080.0120.0530.1790.113
product_description_lenght-0.0300.0310.0090.117-0.0320.000-0.009-0.0160.0530.0550.0090.033-0.013-0.0100.1690.2111.0000.134-0.0200.0730.1110.0360.095-0.0810.0020.0150.1120.0020.007
product_height_cm-0.0040.019-0.0130.2840.0190.0030.0020.016-0.014-0.0080.0100.1060.0110.0090.3060.3280.1341.0000.244-0.057-0.0800.7640.5300.3340.0170.0180.0660.0490.061
product_length_cm0.0050.009-0.0030.2820.0080.0020.0100.010-0.067-0.0800.0040.1090.0320.0200.2310.268-0.0200.2441.0000.0600.0050.7350.6180.6310.0180.0180.0850.0620.078
product_name_lenght-0.0100.0160.0130.033-0.0220.007-0.000-0.0040.0190.030-0.0060.016-0.004-0.0030.0240.0420.073-0.0570.0601.0000.1630.0220.0760.0660.0080.0130.0700.0090.001
product_photos_qty-0.0190.0260.0020.010-0.064-0.0020.004-0.000-0.022-0.0090.001-0.003-0.004-0.004-0.0120.0280.111-0.0800.0050.1631.000-0.0310.003-0.015-0.0070.0160.040-0.078-0.029
product_volume_cm0.0040.014-0.0100.3690.0150.0020.0100.017-0.056-0.0600.0090.1480.0270.0220.3510.3910.0360.7640.7350.022-0.0311.0000.7670.7620.0240.0190.0350.0730.081
product_weight_g-0.0180.026-0.0090.4480.0010.0000.0160.021-0.050-0.0550.0070.1980.0280.0260.4520.5150.0950.5300.6180.0760.0030.7671.0000.6190.0230.0210.0790.0950.101
product_width_cm0.010-0.002-0.0140.273-0.003-0.0020.0120.013-0.058-0.0610.0050.1250.0260.0250.2340.273-0.0810.3340.6310.066-0.0150.7620.6191.0000.0190.0130.0580.0750.043
review_response_time-0.0210.023-0.0560.0170.0180.012-0.004-0.013-0.010-0.027-0.0170.005-0.014-0.0170.0240.0080.0020.0170.0180.008-0.0070.0240.0230.0191.0000.0040.0110.017-0.015
review_score0.0490.0420.0960.0150.0410.0120.0130.0560.0190.0630.1310.0280.0120.0090.0280.0120.0150.0180.0180.0130.0160.0190.0210.0130.0041.0000.0230.017-0.221
seller_state0.0540.0660.0500.0480.0000.0100.0110.0370.0840.0400.0290.0330.0160.0210.0370.0530.1120.0660.0850.0700.0400.0350.0790.0580.0110.0231.000-0.751-0.068
seller_zip_code_prefix-0.0450.060-0.1390.256-0.0120.001-0.0040.004-0.038-0.0380.0050.0660.0060.0030.1630.1790.0020.0490.0620.009-0.0780.0730.0950.0750.0170.017-0.7511.0000.123
time_to_delivery-0.4000.4270.3070.420-0.022-0.040-0.0070.047-0.093-0.164-0.0030.0390.009-0.0720.1230.1130.0070.0610.0780.001-0.0290.0810.1010.043-0.015-0.221-0.0680.1231.000

Missing values

2023-12-06T11:43:38.777969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-06T11:43:40.243453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-06T11:43:41.075795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-187c396fd4830fd04220f754e42b4e5bff3149Sao PauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.721Credit Card118.12a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-112017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares18.0-8.01976.020172017-1010OctMon
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-187c396fd4830fd04220f754e42b4e5bff3149Sao PauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.723Voucher12.00a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-112017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares18.0-8.01976.020172017-1010OctMon
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-187c396fd4830fd04220f754e42b4e5bff3149Sao PauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.722Voucher118.59a54f0611adc9ed256b57ede6b6eb51144NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-112017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares18.0-8.01976.020172017-1010OctMon
3128e10d95713541c87cd1a2e48201934a20e8105f23924cd00833fd87daa0831delivered2017-08-15 18:29:312017-08-15 20:05:162017-08-17 15:28:332017-08-18 14:44:432017-08-283a51803cc0d012c3b5dc8b7528cb05f73366Sao PauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-21 20:05:1629.997.781Credit Card337.77b46f1e34512b0f4c74a72398b03ca7884NaNDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.2017-08-192017-08-20 15:16:36utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares12.0-10.01976.020172017-0818AugTue
40e7e841ddf8f8f2de2bad69267ecfbcf26c7ac168e1433912a51b924fbd34d34delivered2017-08-02 18:24:472017-08-02 18:43:152017-08-04 17:35:432017-08-07 18:30:012017-08-15ef0996a1a279c26e7ecbd737be23d2352290Sao PauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-08 18:37:3129.997.781Credit Card137.77dc90f19c2806f1abba9e72ad3c3500735NaNSó achei ela pequena pra seis xícaras ,mais é um bom produto\r\n2017-08-082017-08-08 23:26:23utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares05.0-8.01976.020172017-0818AugWed
5bfc39df4f36c3693ff3b63fcbea9e90a53904ddbea91e1e92b2b3f1d09a7af86delivered2017-10-23 23:26:462017-10-25 02:14:112017-10-27 16:48:462017-11-07 18:04:592017-11-13e781fdcc107d13d865fc7698711cc57288032FlorianopolisSC187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-31 02:14:1129.9914.101Boleto144.091bafb430e498b939f258b9c9dbdff9b13NaNNaN2017-11-082017-11-10 19:52:38utilidades_domesticas40.0268.04.0500.019.08.013.09350MauaSPHousewares214.0-6.01976.020172017-1023OctMon
65f49f31e537f8f1a496454b48edbe34da7260a6ccba78544ccfaf43f920b7240delivered2017-08-24 11:31:282017-08-24 11:45:252017-08-25 14:17:552017-08-28 20:12:202017-09-147a1de9bde89aedca8c5fbad489c5571c1315Sao PauloSP2be03d93320192443b8fa24c0ca6ead983504c0cb71d7fa48d967e0e4c94d59d92017-08-30 11:45:2546.8067.701Credit Card1127.458899ca945efd951c97107b49662892271NaNPrezados que porcaria de atendimento ao cliente não se consegue falar com um atendente, só uma máquina burra e ignorante.\r\nVocês não me enviaram o kit de vedação da cafeteira e o filtro e se só isso n2017-08-292017-08-30 02:26:02utilidades_domesticas59.0189.03.0775.016.016.013.09350MauaSPHousewares14.0-17.03328.020172017-0811AugThu
71fa40f202d5d233b6491e976c557b82250fd5707c28d0a64dc20d67f937dd9badelivered2017-09-23 22:11:102017-09-23 22:25:112017-09-26 17:27:542017-10-19 21:09:212017-11-1335c6ec4630637b3ec0da6e587f245f8369043ManausAM18415b1dae10d2dcb36beec370c6a90cd3504c0cb71d7fa48d967e0e4c94d59d92017-09-27 22:25:1128.9021.151Credit Card150.054b70092fc12f2328972d5ff1022d87e94NaNNaN2017-10-202017-10-23 04:13:41utilidades_domesticas59.0322.05.0600.024.05.018.09350MauaSPHousewares325.0-25.02160.020172017-0922SepSat
841c045db2d1876be9f05cf4a787693b2a286f46d6e54cc0179bbb0ee07b0df5edelivered2017-08-16 14:06:302017-08-16 14:55:202017-08-18 15:41:402017-08-21 15:05:132017-08-294e4fa2b85379e9db6dc59f873f0a97485640Sao PauloSP15e18248fc768bdb7fc69fd012068d1093504c0cb71d7fa48d967e0e4c94d59d92017-08-22 14:55:2024.907.781Credit Card132.68b9e086024ceb0234e5950016497a49285NaNNaN2017-08-222017-08-26 02:31:53utilidades_domesticas37.0749.04.0600.016.06.020.09350MauaSPHousewares45.0-8.01920.020172017-0814AugWed
9a60241fca336b3f14485dff30a172cab2a3caad976ff659d519660a7c9357122delivered2018-07-27 22:05:262018-07-27 22:24:212018-07-30 09:15:002018-08-02 13:45:012018-08-200b7a92bbb834394fd894c370ba56827888960SombrioSC1883cea107372a7f07b5830904f74952d1c40343cc5d18c2d8248ac2f3366de342018-07-31 22:24:2164.9923.261Credit Card2176.50abf7167b43e3978411cfaa0f77e5d21b3NaNNaN2018-08-032018-08-03 21:50:00utilidades_domesticas53.0220.01.01125.036.043.036.013482LimeiraSPHousewares05.0-18.055728.020182018-0722JulFri
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115601937592924b66482b823ee7ecd185d0ff0fbd856ba1d4961786fb54bd448eb7fedelivered2018-03-31 19:17:432018-03-31 19:55:182018-04-02 19:10:462018-04-06 22:05:042018-04-1896328ac15f58fbb232fe14b18210338289675Vargem BonitaSC10c800efe70e04ffcc3b266946e3e4826c731d18cea9bf687ffee82a241c25b112018-04-05 19:55:18389.0037.701Credit Card8426.7045c02db2f81c1968c58f08d46694213d4NaNNaN2018-04-072018-04-18 13:28:14la_cuisine59.0284.02.012500.083.021.043.089701ConcordiaSCLa Cuisine116.0-12.074949.020182018-0319MarSat
1156024cbf1cc60a2d1704a70e11ee8be1510a406c8e1382162dc6bef214e0c01fc297delivered2018-01-01 17:03:132018-01-01 17:11:482018-01-02 15:58:132018-01-10 13:45:302018-01-309b8844d7cceb1277e6508cce966e4a096767Taboao Da SerraSP12365562e74dd46f5e99cdc696c504ceac731d18cea9bf687ffee82a241c25b112018-01-05 17:11:18112.0018.031Credit Card2130.03d2ab760b550d788a438048f6fca52aef5NaNNaN2018-01-112018-01-12 09:53:28la_cuisine49.0554.01.01750.095.09.037.089701ConcordiaSCLa Cuisine18.0-20.031635.020182018-0117JanMon
115603fbd9022ebf9271e1952ca884a972d1bd3e46b833c6f5d7827700b8ef99db2e08delivered2017-07-22 17:55:132017-07-22 18:10:092017-07-24 18:28:012017-07-27 19:03:292017-08-11578035514ad8238fa724f24b4aabc1df88705TubaraoSC12365562e74dd46f5e99cdc696c504ceac731d18cea9bf687ffee82a241c25b112017-07-27 18:10:09105.0014.601Credit Card1119.60ac24486e719028c654916b3aa2419a505NaNNaN2017-07-282017-07-29 14:03:58la_cuisine49.0554.01.01750.095.09.037.089701ConcordiaSCLa Cuisine15.0-15.031635.020172017-0717JulSat
11560430b0ea32347476f4b427daf62e09a5bb9d9cfbce48737214f23bf294daf3c6f7delivered2017-08-12 19:20:392017-08-12 19:30:172017-08-14 18:26:282017-08-17 18:28:102017-09-056c861f46d09c560422f6ba6b33c017fe9371MauaSP1b52e7c92200ed9874e77bc09b5ed4d40c731d18cea9bf687ffee82a241c25b112017-08-17 19:30:17174.0039.061Credit Card5213.06fdbbec4b750e768ac12c054acd906d1d5NaNNaN2017-08-182017-08-19 20:09:58la_cuisine57.0232.01.07600.055.015.055.089701ConcordiaSCLa Cuisine14.0-19.045375.020172017-0819AugSat
1156051ce0acf125f1bcd636276dd213363196d11524bb77c28efad04e4467eac8a660delivered2017-07-31 18:10:292017-07-31 18:25:202017-08-02 18:22:302017-08-09 20:38:072017-08-286968d41eb700f1ea39424e04b854bf7e30130Belo HorizonteMG1cc9e875c2df286dbed83efe01191162cc731d18cea9bf687ffee82a241c25b112017-08-04 18:25:20129.0026.181Credit Card255.184dc7a70acb9d9eeeb1db4e74f88b2e9b5NaNRecomendo2017-08-102017-08-13 17:29:51la_cuisine57.0429.01.02700.085.07.040.089701ConcordiaSCLa Cuisine39.0-19.023800.020172017-0718JulMon
1156061ce0acf125f1bcd636276dd213363196d11524bb77c28efad04e4467eac8a660delivered2017-07-31 18:10:292017-07-31 18:25:202017-08-02 18:22:302017-08-09 20:38:072017-08-286968d41eb700f1ea39424e04b854bf7e30130Belo HorizonteMG1cc9e875c2df286dbed83efe01191162cc731d18cea9bf687ffee82a241c25b112017-08-04 18:25:20129.0026.182Voucher1100.004dc7a70acb9d9eeeb1db4e74f88b2e9b5NaNRecomendo2017-08-102017-08-13 17:29:51la_cuisine57.0429.01.02700.085.07.040.089701ConcordiaSCLa Cuisine39.0-19.023800.020172017-0718JulMon
115607c72888e51a36defb7b5d49201fcbccf7c3cd86c3cbac654f8558a8286ba93c1cdelivered2017-07-11 10:45:572017-07-11 10:55:142017-07-11 19:14:482017-07-19 17:27:432017-08-04d8b80a0bd7560fab10e804f36d99fe3390220Porto AlegreRS1724c49c346d2979339d366fa59ce49de3078096983cf766a32a06257648502d12017-07-17 10:55:14119.9927.161Credit Card147.1580d6f91212fb5216bfc90feddee755815NaNRecebi corretamente o produto no prazo estipulado.2017-07-202017-07-21 13:13:33la_cuisine33.0532.01.03600.030.030.030.013720Scao Jose Do Rio PardoSPLa Cuisine18.0-16.027000.020172017-0710JulTue
115608c72888e51a36defb7b5d49201fcbccf7c3cd86c3cbac654f8558a8286ba93c1cdelivered2017-07-11 10:45:572017-07-11 10:55:142017-07-11 19:14:482017-07-19 17:27:432017-08-04d8b80a0bd7560fab10e804f36d99fe3390220Porto AlegreRS1724c49c346d2979339d366fa59ce49de3078096983cf766a32a06257648502d12017-07-17 10:55:14119.9927.162Voucher1100.0080d6f91212fb5216bfc90feddee755815NaNRecebi corretamente o produto no prazo estipulado.2017-07-202017-07-21 13:13:33la_cuisine33.0532.01.03600.030.030.030.013720Scao Jose Do Rio PardoSPLa Cuisine18.0-16.027000.020172017-0710JulTue